SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6201–6225 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle—0
Graph Value Iteration—0
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning—0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control—0
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey—0
"Guess what I'm doing": Extending legibility to sequential decision tasks—0
Safe reinforcement learning control for continuous-time nonlinear systems without a backup controller—0
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Measuring Interventional Robustness in Reinforcement LearningCode0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation—0
Multi-level Explanation of Deep Reinforcement Learning-based Scheduling—0
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective—0
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes—0
Evolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks—0
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems—0
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
Selective Token Generation for Few-shot Natural Language GenerationCode0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified